Accelerated Innovation

Assessing Short-Listed LLMs

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Assessing Short-Listed LLMs

Description

This capability focuses on systematically evaluating a small group of pre-selected LLMs to determine which one performs best for your specific business objectives. It includes defining test scenarios, running structured evaluations, and comparing results across key metrics.

Why it's Important

Shortlisting alone does not guarantee success. Teams must rigorously test candidate models to make informed decisions. Structured assessment helps ensure models are evaluated fairly and consistently across quality, speed, cost, safety, and integration complexity. This approach reduces selection bias, uncovers hidden risks, and increases confidence in deploying the best-fit model for your use case.

Why it's Challenging @ Scale

  • Evaluations require high-effort setup: Designing fair test scenarios and building consistent inputs can be time-intensive.
  • Many teams lack standardized metrics: Without shared benchmarks, model comparisons can become subjective.
  • Different models may require different prompts: Getting comparable results can be hard if model behavior varies significantly.
  • Costs and latency differ across providers: Comparing pricing, speed, and quality simultaneously introduces tradeoffs.
  • Evaluations may not generalize: A model that performs well in a controlled test may struggle in real-world use.

Complexity

High: Maturing this capability requires reusable evaluation frameworks, clear scoring rubrics, toolchain integration, and input from both technical and business stakeholders.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Evaluating and Selecting the Best Model(s) for Your GenAI Solution workshop (2 hrs.) to understand foundational key concepts and explore applied best practices:
  • Outlining the Model Evaluation Lifecycle
  • Understanding Model Types and Capabilities
  • Aligning Evaluation to Solution Objectives
  • Comparing Commercial vs. Open Source Options
  • Establishing a Reusable Evaluation Framework
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy:
  • Align on your Current State and define your Target State
  • Create an actionable enablement plan
  • Define target timeline and measures of success
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame:
  • Design a Pilot Evaluation Scenario: Choose one real use case and create standardized prompts and success criteria.
  • Run a Manual Side-by-Side Test: Compare outputs from 2-3 short-listed models using a spreadsheet and human scoring.
  • Document Early Findings: Capture qualitative observations and quantitative scores to inform future assessments.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including::
  • Defining Your Model Objectives & Requirements
  • Model Evaluation Data Assessment and Prep
  • Selecting In-Scope Models
  • LLM Evaluation
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale:
  • Assess Your Proposed Solution or Process: Review whether current evaluation methods are yielding clear, actionable results.
  • Define in-scope Processes and Guardrails: Set boundaries for what success looks like, including minimum performance thresholds.
  • Close any Data or Measurement Gaps: Make sure all short-listed models are tested using consistent inputs, labels, and scoring rubrics.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units:
  • Define Your Phased Implementation Plan: Determine when and how model assessments will feed into approval and deployment steps.
  • Build Awareness and Finalize Enablers: Provide toolkits and templates to help other teams replicate your evaluation approach.
  • Operationalize Your Comms Plan: Share test results and lessons learned with decision-makers and model stakeholders
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases:
  • Create an Evaluation Rubric Template: Provide a shared format for scoring model quality, accuracy, and usability.
  • Maintain a Library of Test Prompts and Tasks: Build a repository of representative examples that reflect core use cases.
  • Track Model Scores and Outcomes: Record how each short-listed model performed to inform future selection and tuning efforts.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers:
  • Enable Teams to Run Their Own Assessments: Give teams access to tools, datasets, and guidelines for LLM comparison.
  • Centralize Results Sharing: Make evaluation outputs easily accessible to reduce duplication and increase transparency.
  • Promote Use of Reusable Scoring Tools: Encourage adoption of shared scripts, scoring UIs, or model comparison dashboards.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum:
  • Highlight a Model Selection Success Story: Share how evaluation enabled confident decision-making.
  • Recognize Contributors to Evaluation Design: Acknowledge those who created rubrics, built tools, or facilitated sessions.
  • Showcase Before-and-After Improvements: Demonstrate how assessments led to better model fit and business outcomes.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine:
  • Integrate Model Assessment into Dev Pipelines: Automate evaluations as part of model deployment or testing flows.
  • Align Evaluation Criteria Across Use Cases: Use shared success metrics so models can be scored consistently across departments.
  • Embed Scores into Model Registry Tools: Include results in model cards or governance dashboards to support traceability.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort:
  • Automate Scoring of Common Tasks: Use LLMs or scripts to rate summaries, classifications, or responses at scale.
  • Trigger Reassessments Automatically: Re-run model evaluations when prompts, datasets, or provider models change.
  • Pre-Populate Evaluation Reports: Auto-generate insights and recommendations based on model performance data.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases:
  • Expand Scoring to Include Risk & Governance: Track compliance and explainability alongside quality scores.
  • Adapt Rubrics for New Model Types: Tailor evaluations to include multilingual, multimodal, or agentic model capabilities.
  • Benchmark Assessment ROI: Measure how evaluation efforts improve adoption, reduce rework, or increase model quality.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Using inconsistent test inputs or scoring: Lack of standardization reduces trust and comparability.
  • Over-indexing on one metric: Favoring speed or cost alone may result in lower quality or relevance.
  • Rushing the evaluation process: Skipping thorough testing can lead to performance or integration issues later.
  • Failing to document evaluation context: Without notes on conditions and methods, results may be hard to interpret or repeat.
  • Letting opinions override results: Stakeholders may favor familiar options even if another model performs better.

Targeted Benefits

While Assessing Short-Listed LLMs can be challenging, its benefits are clear and compelling, including:

  • Stronger model decision-making: Objective testing enables more confident and transparent selection.
  • Faster alignment across teams: Shared results reduce debate and speed up buy-in.
  • Improved evaluation repeatability: Standard tools and methods support scaling to new use cases.
  • Better understanding of tradeoffs: Side-by-side results clarify where models excel or struggle.
  • Higher model adoption and performance: Choosing the right model improves solution impact and usability.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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